A Funding Methodology With Adjustment for Hospital Quality Performance
Bibliographic record
Abstract
Rationale: Improving hospital quality performance while improving outcomes and efficiency and effectiveness of a provincial hospital system has been paid high attention. It requires predictable, stable, and multi-year funding with accountability by provincial government. Modeling of multi-year-funding with quality performance adjustment is essential in this processing, required to reach equity, reflect hospital performance and resource utilization with high quality. Objective: To model the quality performance adjusted funding formula based on the original multi-year funding strategy for the hospitals in a province with adjustment for several major factors to reach equity based on the data in the past several years using the statistical models. Methodology: Two quality performance indicators, readmission rate and mortality rate, were chosen as major indicators, which affect the volume and unit cost. To assess the quality performance with equity, GLM (Generalized Linear Model) model was applied in standardizing the readmission rate and mortality rate adjusted for several factors and the ratio of real rate vs. expected rate were calculated. The volume (weighted cases) and unit cost (cost per weighted case) were adjusted for the standard ratios of readmission and mortality. Bayesian model were employed in the recalculation of the weighted cases and cost per weighted cases. The expected weighted cases for individual hospitals were calculated using GLM model with adjustment for regional socio-economic status, community population, age group and gender. The expected unit cost (cost per weighted case) for individual hospitals were calculated using composite weighted robust regression model. Results: The expected readmission rate and mortality rate were calculated by the probabilities of events under the certain conditions using GLM model with appropriate distribution. The adjusted weighted cases for each hospital were recalculated using Bayesian model. The multi-year unit cost formula with quality performance was established. The funding plan could be developed based on those results above to promote hospitals to improve their quality while improving efficiency and effectiveness. Discussions: It is necessary to reflect the quality performance in the multi-year funding methodology. It can reach equity better and encourage hospitals to improve their service quality, and real efficiency and effectiveness. It is also essential to recalculate the volume and unit cost with appropriate adjustment for readmission and death in order to make the funding plan more efficiently.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".